Experiences with Experiential Learning: Learning from Our Own Experiential and Conceptual Insights
Bibliographic record
Abstract
Universities have been moving for decades toward experiential learning, evidenced by the rise of co-operative education programs, study abroad, and the integration of community partners into university course projects. Yet, experiential learning can also be enacted by faculty and students on a smaller scale. Immersing students into new experiences is an excellent base for learning, but it must be supported by other learning elements as well (e.g., critical reflection, integration with abstract concepts, application of new insights). According to experiential learning theory, it is the process of navigating dialectical tensions in connecting and transforming insights from both experience (feeling) and thinking (abstract concepts) that lies at the heart of learning. The experiencing and applying aspects of the learning cycle can be accomplished in many different ways, as can the reflection and thinking aspects of the cycle, and the process of moving through all four modes can be supported by faculty who can flexibly adapt and join students in a learning journey. In this SOTL conversation, we, a mature student with rich life experiences and diverse educational experiences (Linda) and a faculty member educated in experiential learning theory (Tiffany), explore some of our own experiences enacting experiential learning and reflect on what we’ve found contributes to an integrative learning experience. Along the way, we discuss our views on how emotional and social intelligence competencies can support the learning process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".